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检索条件"机构=Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational"
108 条 记 录,以下是31-40 订阅
排序:
A Many-Objective Evolutionary Algorithm Based on New Angle Penalized Distance
A Many-Objective Evolutionary Algorithm Based on New Angle P...
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Congress on Evolutionary Computation
作者: Junchao Fang Wei Fang Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China
In evolutionary many-objective optimization, achieving better balance between convergence and diversity of the population is a crucial way to improve the efficiency of the algorithm. However, diversity measure may sel... 详细信息
来源: 评论
Spectral Efficiency for Multi-pair Massive MIMO Two-Way Relay Networks with Hybrid Processing  8th
Spectral Efficiency for Multi-pair Massive MIMO Two-Way Rela...
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8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019
作者: Wang, Hongyan Li, Zhengquan Xue, Xiaomei Li, Baolong Liu, Yang Wu, Guilu Wu, Qiong Graduate School of Engineering Nagasaki University Nagasaki852-8521 Japan Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China National Mobile Communication Research Laboratory Southeast University Nanjing210096 China
In millimeter wave (mm-wave) communication systems, large antenna array can be employed for higher data rates. Hybrid digital and analogue beamforming design is adopted to reduce the digital signal processing (DSP) po... 详细信息
来源: 评论
An Efficient and Flexible Automatic Search Algorithm for Convolution Network Architectures
An Efficient and Flexible Automatic Search Algorithm for Con...
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Congress on Evolutionary Computation
作者: Liang Zhao Wei Fang Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China
As many superior convolutional neural networks (CNNs) have been proposed in recent years, CNNs have played an important role in computer vision. However, manually-designing CNN architecture is difficult since expertis... 详细信息
来源: 评论
A Dual-Branch Network for Infrared and Visible Image Fusion
A Dual-Branch Network for Infrared and Visible Image Fusion
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International Conference on pattern recognition
作者: Yu Fu Xiao-Jun Wu Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China
In recent years, deep learning has been used extensively in the field of image fusion. In this article, we propose a new image fusion method by designing a new structure and a new loss function for a deep learning mod... 详细信息
来源: 评论
A Grassmannian Manifold Self-Attention Network for Signal Classification  33
A Grassmannian Manifold Self-Attention Network for Signal Cl...
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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
作者: Wang, Rui Hu, Chen Chen, Ziheng Wu, Xiao-Jun Song, Xiaoning School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China Department of Information Engineering and Computer Science University of Trento Trento Italy
In the community of artificial intelligence, significant progress has been made in encoding sequential data using deep learning techniques. Nevertheless, how to effectively mine useful information from channel dimensi...
来源: 评论
Editorial
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International Journal of Bio-Inspired Computation 2020年 第2期16卷 67-67页
作者: Fang, Wei Wu, Xiaojun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and Technology Jiangnan University 214122 China
来源: 评论
A dual-branch network for infrared and visible image fusion
arXiv
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arXiv 2021年
作者: Fu, Yu Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China
In recent years, deep learning has been used extensively in the field of image fusion. In this article, we propose a new image fusion method by designing a new structure and a new loss function for a deep learning mod... 详细信息
来源: 评论
A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop
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Complex System Modeling and Simulation 2023年 第1期3卷 47-58页
作者: Zaihe Cheng Wei Shen Wei Fang Jerry Chun-Wei Lin School of Internet of Things Wuxi Institute of TechnologyWuxi 214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergen 5020Norway
High-utility itemset mining(HUIM)can consider not only the profit factor but also the profitable factor,which is an essential task in data ***,most HUIM algorithms are mainly developed on a single machine,which is ine... 详细信息
来源: 评论
Infrared and visible image fusion using Latent Low-Rank Representation
arXiv
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arXiv 2018年
作者: Li, Hui Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of IoT engineering Jiangnan University Wuxi China
Infrared and visible image fusion is an important problem in the field of image fusion which has been applied widely in many fields. To better preserve the useful information from source images, in this paper, we prop... 详细信息
来源: 评论
Feature Space Renormalization for Semi-supervised Learning
arXiv
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arXiv 2023年
作者: Sun, Jun Mao, Zhongjie Li, Chao Zhou, Chao Wu, Xiao-Jun The School of Artificial Intelligence and Computer Science Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi214122 China The Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi214122 China
Semi-supervised learning (SSL) has been proven to be a powerful method for leveraging unlabelled data to alleviate models' dependence on large labelled datasets. The common framework among recent approaches is to ... 详细信息
来源: 评论